They are all respiratory medicine specialists, but they are not familiar with all biomarkers used in this research.

the comparison with the specialists should be seen not only from the performance evaluation of this study but also from the perspective of supplementing the specialists.


The GEC mainly constitutes a generative task, i.e., a task that produces a grammatically correct sentence from a given original sentence whereby multiple distinct outputs can be judged "correct" for a single input.

automatically evaluating the performance is not straightforward and is considered as an important issue as in the fields of translation and summarization.


In order to cover more aspects about the topic and to avoid redundancy, we would like to assign high important scores on more diverse chains.

we utilize DivRank (Mei et al., 2010), a well-known diversified ranking algorithm, for calculating the ranking scores for different chains.


We find that taking seven top-ranking subtopics (chains) can lead to a good coverage of the given topic, and selecting four sentences for each subtopic to form a paragraph will make the overall length just around 1,000.

we limit the numbers for subtopic and sentence selection as such.


Preliminary experiments suggest that most elements share high similarity values due to our selection criteria in previous steps, causing simple greedy selection strategy to fail.

we consider a different method: ordering sentences and paragraphs according to its position in the original article.


The diversity of possible essays makes automatic evaluation metrics that count on content overlaps impossible since system outputs can then only be compared with rather limited references.

we leave proper design of automatic metrics as future work and only perform manual evaluation in this study.


Character and word vocabularies were constructed from those appeared more than nine times in training and development datasets 5 .

we chose 2,709 characters from 3,630 characters.


In this paper, we explored the effect of conveying understanding results of user utterances in a chatoriented dialogue system by an experiment using human subjects.

we found that only certain types of understanding results, such as those related to a user's permanent state, are effective to improve user satisfaction.


Note that, in this paper, we focus on the effects of system utterances that convey understanding results "as they are"; that is, utterances are literally the same as understanding results.

of the experiment, we found that user's temporary states during dialogue should not be conveyed and user's permanent states and information irrelevant to users themselves can be conveyed safely as system utterances.


In addition, it is natural that debates on some topics are more prominent, indicating the importance of the topic.

being able to understand the prominence of a topic and the levels of contrastive sentiment will help one to prioritise actions.


However, manual production of texts from scratch for each target population separately cannot keep up with the amount of information which should be accessible for everyone.

many systems for automatic lexical simplification (LS) of texts have been proposed.


Each model generates four responses from one query utterance by beam sampling (beam width 3) using four different random seeds.

the total number of responses generated by one model is 200.


• Proofread: A meeting where people who have lost their families to cancer discuss ... .There are ten members in their 30s to 70s.

it may be worth exploring not only discarding instances where additional information has been added to the proofread sentences but also building a model that considers other sentences appearing near the source sentence.


Paraphrase questions are seldom entirely equivalent with the reference question.

when question swapping is used to augment the training examples, it will inevitably introduce some noise.


However, using all DRLM examples hurt Se-mEval performance, which was not surprising given the substantial difference between SemEval and DRLM questions.

we ran an additional evaluation in which we combined the weighted scheme from Section 3.2.1 with the LMbased selection from Section 3.2.2.


As we can see in row c), the percentage of ties differs across languages: the Dutch Pilot 2 was evaluated as equal to Pilot 0 only in 5.65%, while for Bulgarian it was in 58.10% of the evaluations.

we cannot compare the relative quality of Pilot 2 and Pilot 0 only based on the number of cases when Pilot 2 was judged strictly better than Pilot 0 (row a), or only based on the number of cases when Pilot 2 was judged better or same as Pilot 0 (row c).


Therefore, we cannot compare the relative quality of Pilot 2 and Pilot 0 only based on the number of cases when Pilot 2 was judged strictly better than Pilot 0 (row a), or only based on the number of cases when Pilot 2 was judged better or same as Pilot 0 (row c).

in the last row e, we report the percentage of non-tying comparisons where Pilot 2 was judged better than Pilot 0, that is, P2 better ignoring ties than P0 (%) equals #(P2 better than P0) #(P2 better than P0) + #(P2 worse than P0) × 100% Figures 3 and 4 provide a graphical representation of Tables 10 and 11, respectively.


As far as the above results are concerned, we infer that (i) the domain of the BabelNet dictionary might have a sig-nificant difference compared to that of the initial training data; and (ii) the entries need to be further cleaned up to obtain more domain-related data.

we propose to use domain-adaptation strategies to verify the contribution of BabelNet as described in the next section.


The lectures have been collaboratively transcribed and translated with the AMARA web-based platform 9 .


Greek, for example, only has around 115,000 16 Wikipedia pages available and many detected topics in the English will not have corresponding Greek Wikipedia pages.

we expect a lower coverage of the implicit evaluation method for low-resource languages as can be illustrated in the following example.


Despite of this fact, these two tasks have almost always been seen and performed completely separated and independent.

data containing error annotations of actual post-edit operations are scarce.


The approach for deriving a new lexicon is highly applicable, language and domain independent, as the distributional model can be acquired without any supervision, and the provided heuristics do not have any bias with respect to languages or domains.

polarity lexicons can be generated in multiple languages, without the need of expensive manual supervision.


In particular, the sentiment of context words, available in an already built lexicon, is shown to contribute in deriving the sentiment orientation of a target word.

the socalled SentiCircle is derived for each target word by considering the contexts in which they appear.


That is, learning the assignment of words to topics z given the observed words w by repeatedly sampling the latent variables z ij regarding the other values of z.

of using the collapsed Gibbs sampler for LDA, we can draw the values of θ and φ.


In general, since we aim for discovering topics in tweet messages which are all labeled under the same hashtag, the number of topics in the general conversation will be relatively low.

the parameters of the Dirichlet distribution used in LDA can be assigned small values in order to render only a few topics allocated to each document.


It is desirable to have independent topics and thus avoiding the appearance of the same word in multiple topics.

a better fit will be a model with fewer overlapping words.


Supposedly, if a stem has a high σ of valence (equivalently for arousal) then the valence ratings of the word are distributed over a wider range of values; and lower values of σ imply that ratings are closer to µ.

we used a probability weight based on the probability density function of each word in ANEW to estimate that the stem's valence (arousal) falls exactly at the mean.


This was also confirmed by checking that K = 3 was the model with fewer overlapping words across topics.

we selected the 3-topic model identified by LDA, so the multiplex graph is composed of K = 3 layers, one per topic.


A layer G i was obtained by retrieving the tweet messages sent by the users about topic i.

each layer contains the set of users participating in the topic conversation, where the same user can participate in more than one topic distribution; that is, the same user can be found in different layers of the network (inter-layer connections).


Obviously, this approach is usually too time consuming for obtaining a good coverage and difficult to maintain when the vocabulary evolves or a new language or domain must be analyzed.

it is necessary to devise a method to automate the process as much as possible.


Speaker Identification (SI), Gender (GR), or Age Recognition (AR).

in the first case we took the ground-truth information about the speaker, gender and age (the G experiments, for Ground truth), whereas in the second series of experiments we deployed the actual SI, GR, and AR models to estimate the corresponding hypothesis (the E experiments, for Estimated).


Moreover, since there are very few friendly recordings we removed them from the database.

we operated only with recordings of 3 labels, namely anger, neutral and non-speech.


The human raters evaluated the perceived emotional state of the speakers for each utterance on a 7-point scale.

on the pleasantness scale, 1 corresponds to extremely unpleasant, 4 to neutral, and 7 to extremely pleasant.


In contrast to the first experiment, these experiments are not free of speaker identification errors.

relatively worse results were expected here.


After that, for each combination of normalisation and speaker ID incorporation methods we calculated average ranks, that is -the nominal value depending on the performance of the system on a particular data set, similar to Friedman's statistic (Theodorsson-Norheim, 1987), (Demšar, 2006).

the best approach will be assigned rank 1, while the runner-up, 2, etc.


Since we did not pay any attention to a significance test while performing the rank calculation, now we performed the paired Student's T-Test, comparing the proposed systems with the baseline approach for each corpus independently.

the box-plots with bold outlines indicate significant difference against the baseline approach with at least p = 0.05.


In the case of the Emo-DB corpus this can be explained by highly unbalanced coverage of emotions by the speakers.

the speakers ID03 and ID10 have only one single utterance with the disgust label.


It means that the algorithm is trained not only on disgust samples of ID03 or ID10 but all speakers from the training set.

on each iteration the model could operate with enough samples of all possible labels, enhancing the probability of proper recognition of a particular sample from the testing set.


We supposed that each dialogue had been initiated by a new user and therefore each speaker in the database has very few utterances.

in each iteration of CV we do not have enough data to build a reasonable speaker identification model (see rather poor SI performance on the LEGO in Figure 2).


As a result, in each iteration of CV we do not have enough data to build a reasonable speaker identification model (see rather poor SI performance on the LEGO in Figure 2).

the performance of the SepE system is much lower than that of the SepG system.


The multi-lingual nature of the project also required datasets in languages other than English.

we are making two datasets publicly available: 1) the Let's Go 2014 collect from Let's Go collected during 2014 and 2) The Movie Ticketing (MT) dataset collected with the MT system developed by Voicweb S.A..


Initially some of the data was annotated using a crowd-sourcing platform which required the number of slots filled per task to be fixed.

we picked the minimum number of turns that we thought sufficient to make a decision whether turn S3 would be an appropriate system answer or not.


However, there are other ways of user's to express their (dis)satisfaction towards the system.

satisfaction was also annotated as a subjective measure of the user experience.


Given that we do not have access to the parser used by the live system , as we had for Let's Go, we couldn't use dialogue acts from both system and user, nor extract the dialogue acts from the transcriptions.

we only used features that could be derived from the transcriptions, such as number of words or words repeated in consecutive turns, combined with the annotations for anger, valence, arousal, barge in, satisfaction and repetition.


Annotations differ with respect to the range of phenomena they cover, their granularity level of defined concepts, segmentation principles and their theory dependencies.

such corpora are not easy to re-use for purposes and apply to domains other than they were originally developed for.


When trained on unigram word token models only we observed a decrease in performance of about 10% compared to the performance using the combined features; trained on n-gram POS tag models 5% on average; and trained on n-gram chunking information 20%.

wording of an utterance is still very important, but, when supplied with linguistic information, the performance of the classifier improves.


However, when the classifier is trained on the combination of the MapTask corpus with other corpora, the performance of the classifier improves.

we concluded that corpus data with a broad spectrum of dialogue phenomena like AMI and Switchboard conversations can be profitably used to train models that have stronger predictive power when applied to new dialogue data from other domains, while MapTask based models are rather limited in this respect.


Dialogue Acts (DA) are fundamental for the analysis of conversations: they carry communicative functions such as question, answer, expression of agreement and disagreement, etc.

the range of applications of DA analysis is quite wide and includes conversation summarization (both spoken and written), dialogue systems, etc.


The utility of abandoning the legacy annotations and manually or semi-automatically re-mapping them to the ISO standard could be tested under two conditions: (1) if the new annotation is equal or superior in supervised DA classification and (2) if it is indeed domain independent and allows both cross-domain application and pooling data from different domains (i.e., data aggregation).

in this paper we presents experiments on the semi-automatic re-annotation of the Italian LUNA Corpus  with DiAML and evaluation of the annotations in  cross-domain and data aggregation settings.


However, due to the overlapping turns (both speakers speaking), an utterance can span several turns.

the dialogue act annotation was preceded by additional utterance segmentation.


In LUNA, on the other hand, the unit of annotation was considered to be an utterance, which is similar to turn, ignoring the other speaker barge-ins.

re-annotation procedure also included re-segmentation.


For the communicative function level classification, we also have observed that the new annotation scheme provides better cross-domain generalization.

indeed, it is worth transferring legacy annotations to the ISO standard.


Furthermore, some studies (Hendrickx and Hoste, 2009;Nicolov et al., 2008) have demonstrated that a coreference resolution system trained on newswire data performs poorly when tested on other text genres.

there is a crucial need for annotated material of more text genres and domains.


Although Wikipedia offers so-called Wikipedia dumps, parsing such files is rather tedious.

we transformed the Wikipedia dump from its original XML format into the Berkeley database format compatible with WikipediaMiner (Milne and Witten, 2008).


In above example (2), a pronominal mention अपने his is referring to concrete entity भाजपा ने ता BJP_leader .

pronoun अपने his has a 'Anaphora-C' relation with भाजपा ने ता BJP_leader .


6 In above example (6), two mentions, पू वर् सलामी ब े बाज f ormer_opener and रमीज Rameez are placed side by side and first noun sequence is showing designation for the second one.

mention रमीज Rameez has a 'Coreference-NounComplement' relation with पू वर् स-लामी ब े बाज f ormer_opener .


These two sets {C, H} and {C} have 2 and 1 elements respectively, and this difference in sets cannot be captured here.

we use Jaccard index also known as the Jaccard similarity coefficient , for comparing the similarity anddiversity of sample sets, to capture this difference as mention in (Artstein and Poesio, 2008).


For example, in the sentence above, both he and message could be the subject of the participle clause.

strictly speaking, this case cannot be unambiguously considered to be grammatical coreference.


they do not form a chain with any other mention.

we present the number of entities consisting of more than one mention (non-singleton entities), and include only these entities in the statistics of alignment in Section 4.


The matches are further constrained in that the anaphoric expressions must contain linguistic evidence that they are coreferent with some already mentioned entity, such as a definite determiner article (the) or a demonstrative word (these, that).

"a kinase" does not match, but "this kinase" can.


In the case of international industries based in non-English speaking countries, the professionals in charge of writing requirements are very often non-native users of English, and these professionals rarely receive adequate training in the use of English for this task.

requirements can contain a relatively large diversity of lexical and grammatical errors.


Instead our approach is to address the problem by exploiting the structure of reading comprehension questions, a typical language learning task: Students' answers are linked to a reading text in standard language; when students answer reading questions, they rely on the given textual material and tend to repeat words or even copy whole phrases from the question or the reading text (referred to as reference texts below), a phenomenon known as lifting.

learner answers tend to have a high lexical overlap with the reference texts.


While a word with the surface form das in German can only be tagged as an article, relative or demonstrative pronoun, only the form dass can occur as a conjunction and was presumably intended in this sentence.

the annotator first normalized das to dass before tagging the word as subordinate conjunction KOUS.


This method only deals with OOV words, but about 25% of orthographic corrections in our manual normalizations concern tokens whose surface form is known in the training data, for example the correction from "das" to "dass".

it is desirable to correct such misspellings that result in an IV word as well.


The core of such a pool should rather consist of stylistically neutral and general-purpose words collected from as many domains and genres as possible (in this case in at least three genres/domains).

out of 69,371 entries in the vocabulary based on Stockholm Umeå Corpus (SUC) (Ejerhed et al., 1992), only 8,215 lemmas were retained as forming the base vocabulary pool.


(2014), in which the authors automatically extracted a list of lexical items from a corpus of texts linked to the CEFR scale.

french L2 learners and teachers have fLELex, a freely available list of 17,871 french words and multi-word expressions (MWE) associated with their frequency distribution across the six CEfR levels.


If we mark the whole sentence as "wrong" only because one of the segments contains WUE, the benefit to the learner will be limited.

we consider a segment as a unit of WUE detection.


We postulate that there is a certain distribution of dependency counts in normal sentences, and the counts of error sentences deviate from the distribution.

we take the count of each type of dependency of Stanford Parser (Chang et al., 2009) output as a set of features.


the A1 level, which was none different in the 51-text sample (Figure 3).

the expert model predicted the majority of the words in the sample as known to the study participants, who had attained a proficiency level higher than the A1 level (i.e.


Indeed, whereas the learners tended to share the same grammatical knowledge, they did not always seem to know the same lexical words.

even though the expert model predicted the grammatical knowledge quite accurately, it did not yet take into account the individual differences that we observed in the learners' lexical knowledge within a given proficiency level.


On the other hand, we observed that an expert model predicting the learner's actual vocabulary knowledge based on the word's CEFR level was relatively accurate, but that it presented some errors with respect to the recall of unknown words.

we can safely state that a graded lexical resource such as FLELex offers a promising solution to the automated assessment of a text's lexical complexity for non-native readers.


That is, Comp(A, B) indicates the maximum proportional error reduction that B offers over A. Complementary rate is agnostic to the set of labels involved, and so can also be applied beyond binary classification.

the positive and negative groups do not feature in its definition.


For the second corpus, one partition is drawn from B and the remaining N − 2 partitions are drawn from A.

the third corpus is a 2 : N −3 mixture, and so on to the last corpus which is drawn entirely from B.


The last setup step was to assign a quality score to the translations.

in line with other approaches to quality (Callison-Burch et al., 2007), we defined a scale of quality that distinguishes between 4 comprehension levels: Q1) no errors or only minor ones that do not affect comprehension; Q2) comprehension problems, but most of the original meaning is still preserved; Q3) severe errors, but some meaning can still be inferred; and finally, Q4) meaning is completely compromised 8 .


However, relying on readability formulae to measure text accessibility is an approach that has many drawbacks (Benjamin, 2012; Dubay, 2004;Siddharthan, 2004), mainly re-lated to the fact that readability formulae only employ surface text features such as word or sentence length.

for example, the Flesch formula (Flesch, 1948) presented below is very widely-used but cannot take into account the difficulty autistic readers have in using abstraction (Minshew et al., 2002) or the difficulty aphasic readers have with passive voice or other syntactic constructions (Berndt et al., 1996).


A general rule of thumb is that texts with Flesch index higher than 65 are considered accessible writing (DuBay, 2004), which is why a manual examination of all documents which fell under the threshold of 65 was performed.

certain documents with Flesch index lower than 65 were not discarded even though they included complex terms, because these terms had been adequately explained and the aim was to familiarise the readers with the jargon used in the genre.


We have confirmed that embeddings trained given dependency parses give the best performance on the NLP tasks.

it is interesting to evaluate the performance of such embedding on the ASR error detection task.


After a manual study of 500 "borderline" documents, a quality threshold of 91% has been experimentally set.

a text whose quality is below 91% is ignored.


In the next round of experiments, we plan to go from term analysis to topic analysis in order to free ourselves from the need to have an explicit term associated to a topic to be able to detect its apparition.

we will be able to more emphasis on investigating the emergence of new topics by using Latent Dirichlet Allocation [Blei et al 2003] and in particular its supervised variant [Blei et al 2007], and to study topic dynamics [Blei et al 2006].


In all of these 21 instances the latter noun was the correct one.

the decision was made to remove collective nouns and the subsequent preposition from the tuple.


Coverage can be improved using stemming techniques, although they tend to increase the level of uncertainty since more words with different meanings are conflated into the same stem.

there are two levels of uncertainty which a CLIR system involving Greek has to deal with: one caused by the stemming process and one due to the natural ambiguity of the language (words with more than one possible translation) (Kotsonis et al 2008).


However, they did not assert whether the extracted spelling error corpora reflect the usual writing process such as writing a document.

we propose a new correctable word-typing game that is more similar to the actual writing process.


Wikipedia is considered an excellent source of texts for IE systems due to its broad variety of topics and advantageous characteristics such as the quality of the texts and their internal structure.

there are some IE systems that work with Wikipedia texts and/or their structured metadata, like Wanderlust (Akbik and Bross, 2009) or WOE (Wikipedia-based Open Extractor) (Wu and Weld, 2010).


The results reflected in Table 3 show that sources with a large amount of content produce triplets for more concepts, as was expected.

wikipedia is the source that offers the most good triplets (those assigned 1 point), followed by Simple wikipedia and Simple wiktionary.


Although the questionnaire did not include specific questions on learning disability (in Italian DSA, Disturbi Specifici di Apprendimento), this theme has been mentioned by several participants to the survey in relation to different subjects such as the training of teachers or the allocation of additional resources.

we exploited the functionalities of PIERINO to analyze the contexts in which DSA were mentioned and their relevance with respect to the other topics of the survey.


They claim that users often try to solve their information need by submitting more than one query, reformulating the initial query.

a process regarding to an information need is often composed by several related queries.


Although approaches based on topic models have received a lot of attention, they still require a considerable amount of manual work and expert knowledge to identify a collective topic for human-readable labels.

in this work we rely on previous work on keyphrase extraction to identify scientific topics (Bordea et al., 2013).


It is estimated that almost one-half of Americans suffer from chronic diseases, but a patient's true disease status will progress over time in a way that may not be reflected in the clinical diagnosis of that disease.

we aim to build a resource for determining chronic disease status that includes timing markers alongside the statuses themselves.


Due to the context of use of the OGL Ontology (at the service of a truly multilingual, wide-scoping linguistic repository), it was crucial that no language was a stronger driver than any other.

we put forward a minimal set of POS tags that were as universally valid as possible and general enough to serve languages of very different typology.


Complementary to the high-level POS classification, we introduced an extensive categorisation for grammatical features.

beyond typical morpho-syntactic categories such as gender, number, case, or tense, we expanded on additional linguistic distinctions that can manifest at the lexical level (e.g., event modality, marker type, aspect).


From these achievements, we could gather a number of skills that merit to be shared.

this article begins with a presentation of ELRA's strategic Data and Language Resource (LR) Management Plan for a wide use by the language communities.


ELDA is pursuing the updated project agenda by investigating precise LT needs for the eCommerce sector.

b2C and C2C sites have been identified as potentially benefiting from MT.


The reason behind this consideration is that compared to non-autistic people, individuals with autism are much more prone to experiencing issues with concentration and attention which may result in fatigue, sensory overload or even meltdowns (Happé and Frith, 2006;Lai et al., 2014).

for the initial stage of this study a total of 9 texts were assessed by the participants.


The selected 9 control participants were also divided into two significantly different groups of "skillful" and "less skillful" readers (U = 5724, N1 = 5, N2 = 4, p < 0.001, two-tailed) ( Table 2).

we had Group A readers including 4 autistic and 4 non-autistic "skillful" readers, which did not differ significantly in terms of answering scores (U = 5512, N1 = N2 = 4, p = 0.129, two-tailed) and a Group B readers consisting of 5 autistic and 5 non-autistic "less skilful" readers with no statistically significant difference in their answers (U = 8244, N1 = N2 = 5, p = 0.193, two-tailed), as shown in Table 2.


Two recordings had technical problems leaving a total of 45 participants, with 15 participants assigned to the control condition, 14 to the direct feedback condition, and 16 to the interactive virtual audience condition.

our multimodal corpus constitutes a collection of 180 public speaking presentations.


Moreover the way a spoken dialog utterance is made is another important element that may trigger a humorous reaction.

we also propose to combine acoustic and language features.


Since the linguistic units we aim to extract in this study are not restricted to any syntactic construction or semantic domain, we use the term FSs based on Schmitt (2010)'s suggestion of using this term as an umbrella one to refer to various types of linguistic units in general.

the current study adopts a practical definition of Arabic FSs which basically concentrates on any type of syntactic construction from different language domains that makes high frequency use of semantically regular phrases.


For instance, in the case of 2-component MWUs the order of precedence of graphs is: AXN, 2XN (a noun preceded by a word that does not inflect in the MWU), N2X (a noun followed by a word that does not inflect in the MWU), NXN.

two MWU forms mašine taložnice and korita trake 'belt troughs' would be both extracted by graph 03 (structure N2X) and graph 06 (structure NXN).


The CP usually provides a redundant number of valency slots for the expression of participants of the action denoted by the given CP.

some valency complements of the light verb and of the predicative noun within CPs typically corefer.


'The television V:Instigator:ACT gives even more opportunities for business.'

of the evaluation, we modified the principles governing the distribution of verbal complements proposed in Section 3.1. in the following way: (i) from the valency frame of the light verb, the following valency complements are expressed on the surface: -ACT, regardless of its coreference of quasi control, -all the remaining complements that are in the coreference of quasi control with any nominal ones, (ii) from the valency frame of the predicative noun, the following valency complements are expressed on the surface: -all complements that are not in the coreference of quasi control with any verbal ones.


Second, for each extracted valency complement, we have applied the above given principles to determine whether it  should be or whether it should not be expressed in the surface structure.

each valency complement of the light verb and of the predicative noun within the given CP was assigned a binary value.


Finally, in sẽntence with the given CP in the PDT, we automatically verified whether each assigned value is correct or not.

we found out that the distribution of 97.0% of valency complements follows the proposed principles.


The main difference lies in the annotation of predicative nouns: only a part of deverbal predicative nouns were assigned all valency complements from their valency frames; the remaining predicative nouns -even if the noun is part of a CPwere assigned only those complements that are expressed in the surface structure.

information on the coreference of quasi control is missing.


For a given language pair, the corresponding parallel corpus is the concatenation of all translations t from all the entities/pages p. These Wikipedia tables are also used for evaluation purposes (cf Section 5.1.).

the 1.7 million expansions and 0.4 million acronyms on which the approach is applied were removed from the parallel corpora.


This is partially due to the fact that this module does not take into account the global, article-wide context, but instead relies on a combination of string distance and the PageRank algorithm.

the error can be explained by the linking formula in Section 3.1.: e.g., the string distance score over the title, the redirect and the disambiguation pages between the entity mention GM and the entity candidate db:Germany (0.32879817) is higher than with the entity candidate db:General_Motors (0.21995464).


Since Twitter has an enormously wide range of users with varying interests and sharing preferences, a significant amount of content is being created rapidly.

mining such platforms can extract valuable information.


Therefore, mining such platforms can extract valuable information.

extracting information from Twitter has become a hot topic of research.


Most paraphrase models are tailored for a data set that they will be applied to.

twitter paraphrase models often make use of hashtags, timestamps, geotags, or require topic and anchor words (Xu et al., 2015b).


However, the second candidate is a perfect paraphrase of the textual entity mention, while the first one is not.

using the similarity between the entity mention string and 2 https://github.com/masha-p/PPRforNED the KB entry title to select among the top-scoring candidates found by PPRSim can solve this problem (step I).


Entity disambiguation models usually assume that every entity mention has a valid KB entry and do not explicitly handle NIL entities.

nILs get clustered using the default one-name-per-cluster strategy.


Finally, NIL candidates, that were not assigned a link at the previous step, get clustered with the most similar NILs or constitute a singleton NIL cluster if no similar mentions can be found (step III).

paraLink combines ppRSim with three additional refining steps based on paraphrase similarity between entity mention strings (Figure 2,3).


Adding paraphrase clustering (step II and III) further improves the B 3 +F score to achieve 79.7% and 80.5% correspondingly.

we show that paraphrase similarity can be efficiently incorporated into the entity linking pipeline and improve the performance.


ParaLink is particularly efficient for this type of data since it combines the power of disambiguation PPRSim model with ability to efficiently cluster misspelled and corrupted names, that are typical for forum posts.

it achieves a better performance on a dataset with more informal documents.


Although the evaluation showed improvement as more data is added to IRIS, the manual evaluators annotated the translation quality rather low.

we will continue to gather English-Irish monolingual or parallel corpora, which can be embedded into IRIS.


In others, when presented with options within a POS category, the system fails to select the most appropriate one given the context e.g., scud is translated as smack, whereas drink would be more appropriate given the context.

of this project a first MT system has been developed for Scots and English, and initial results compare favourably with those published for other language pairs within the platform.


The proposed method was applied to all combinations of two and then all three APIs.

seven different translations for each source sentence were obtained.


It is known that BLEU scores cannot be used for comparing two MT systems with different architectures (TectoMT and PBSMT in our case), but only for comparing different versions of the same system (either PBSMT or Tec-toMT in our case).

we focused on the results of the IT1+TERM experiments -which led to the best BLEU score for the TectoMT system, and the second best BLEU score for the PBSMT system (Table 2) -and performed human evaluation and in-depth error analysis in order to compare the performances of the TectoMT and PBSMT systems on the same datasets.


This log also provides information regarding the morphological errors in the MT output.

the substitution log can serve as a valuable resource for training a statistical automatic post-editing system.


The collected corpus is noisy and contains some non-German as well as non-English words 7 http://www.statmt.org/wmt15/translation-task.html and sentences.

we applied automatic language identification (Shuyo, 2010) on the English-German parallel data as well as monolingual German corpus.


We consider that one factor of these disadvantages is granularity of NE classes, since coarse NE labels are  mixed multiple types of fine-grain NE classes that are hard to recognize and transliterate.

we focus on a few fine-grain NE classes in accordance with importance and accuracies of the NE classes for target corpus.


Applications such as bilingual lexical extraction prefer high precision word alignments, while others such as SMT might prefer high recall alignments (Och and Ney, 2004).

evaluation of word alignments typically include both intrinsic and extrinsic metrics.


Each word will be represented by a comparatively low-dimentional real-valued vector and the lexical similarity between words can be evaluated by the Euclidean distance between their correspondent vectors (Mikolov et al., 2013;Pennington et al., 2014).

word vectors can be used as lexical features in various NLP applications and have achieved many positive results (Collobert et al., 2011;Maas et al., 2011).


Taking m × n matrix as input, a convolutional filter with the kernel size of h × n will be applied, in which 1 ≤ h < m. Because the value of n is fixed, the filter acts as a sliding window which moves only vertically and works with every h continuous words within the sample.

a feature map with m − h + 1 elements will be generated.


We believe there are two reason for this phenomenon: the first is that the QUESTION samples in the training set are not sufficient; the second, which we consider as the more crucial one, is that the sample we apply, a 5-word sequence, does not cover the sentence beginning, where the typical question pattern locates, such as "what do you ..." or "how can I ...".

the incompetence of proposed model in detecting question mark is understandable, also still disappointing.


Fully Annotated Corpus: Traditionally a training sentence for dependency parsers contains the dependency relationships among all its words.

it implies that the sentence is divided into words but it does not need POSs for the words.


For the experiments, we used a part of BCCWJ (Maekawa, 2008) annotated with dependency trees provided by .

the sentences are divided into words annotated with POS tags and tree structures.


For example, when we think of new NLP applications, the texts may differ from the general domain texts and we encounter the problem that the accuracy of the default model is very low.

in the experimental settings the baseline is the model trained only from a general corpus and dictionary, denoted by --/--/--meaning that we use no additional language resources for WS, PT, and DP.


We add the training corpus in the target domain discarding the dependency information.

the corpus contains word-POS pair sequences.


This is because WS of OY is difficult, and language resources improve the coverage of OY.

for OW it is not a good strategy to annotate a target corpus with word segmentation only (Wf/--/--).


In addition, dependency annotation is more costly than POS annotation.

the POS annotation is much more effective than dependency annotation of OW.


For example, when we handle well-written texts of limited vocabulary, an effective language resource is a word-POS pair sequence corpus.

it is important to check out the characteristics of the target domain and select a language resource addition strategy for an efficient DP accuracy improvement.


For example, content words such as verbs and nouns are typically directly connected using the dep:conj tag with coordinating conjunctions being dependent on these content words via the dep:cc tag, whereas the output that was obtained from Frog typically represented the dependency through the coordinating conjunction.

only around 10% of the corpus was annotated manually from the initial A2DC+Frog parse.


Although example-based querying has many advantages, querying a treebank using XPath directly enhances the query flexibility compared to the example-based approach.

the second way of querying the corpora in GrE-TEL consists of directly formulating an XPath query describing the syntactic pattern the user is looking for.


In the latter, mere numbers are used as a notation for the position of the token with respect to the next one in the input, numbered 0.

positive integers refer to tokens in the input to be processed, whereas negative integers refer to the tokens on the stack.


For example, the Dutch grammar contains the following rule: This hints that there is something special about the verb zijn, compared to the other verbs.

looking at the lexicon, we find zijn in the following entries: zijn:zijn<det><pos><mfn><pl> zijn:zijn<det><pos><mfn><sg> zijn:zijn<vbser><inf> zijn:zijn<vbser><pres><pl> we add special entries for these: in addition to the anonymous "det pos mfn pl" reading, we add "zijn det pos mfn pl".


In the particular case of Old French, covering a time span more than three centuries, graphical normalisation is even more complicated, since rules would have to be sensitive not only to particular periods but also to particular regions.

no normalisation was applied, and all tools were trained on the original graphical form.


• It has been mentioned that parsing accuracy highly depends on correct pos tagging.

when only sentences with tagging errors are evaluated, LAS accuracy is low, but the JTP performs slightly better (line 6).


It depends on a resumptive pronoun, e.g.

the locative (en) in (1) or the subject (il) in (2): ( ModD normally occurs with long-distance dependencies.


The system workflow is based on representation models applied to all resources, which represent objects of linguistic processing, namely Knowledge Bases (KBs), Web pages and full texts.

we develop an architecture, which takes advantage from the semantic information stored in Linguistic Resources (LRs) and is based on the integration of NooJ.


However, transcribing speech is a time and cost consuming manual process and so far only 380 hours of ILSE interviews have been transcribed.

it is the aim of our work to establish technical systems to fully automatically transcribe the ILSE interview data.


Speaker changes and utterance boundaries were transcribed but not marked in the interview recordings, and no time alignment between recording and transcript was created.

one transcription is provided either per part of the interview (one side of a tape, ∼45 min) in T1 and T2 or per full interview (0.3-2 hours) in T3 and T4.


Little attention was paid to the audio quality, and automatic speech processing was not envisioned.


the position and orientation of the microphone placed on the table.

the data is rather noisy, including reverberation, table tapping, shuffling paper, writing, placing objects, in addition to environmental noise from the tape recorder, the street, and alike.


Emotional Speech: The content of the biographic interviews covers a wide collection of topics, ranging from family and career to very private questions like sexual experiences.

major parts of the interviews contain emotional speech with strongly varying arousal and valence (Schlosberg, 1954;Banse and Scherer, 1996).


resulting in inconsistent file formats and structure.

the transcriptions were first converted into a consistently structured text format.


While the VAD procedure splits the audio recording into a number of partitions, the audio recording's transcription remains unpartitioned.

we start a long audio alignment procedure which iteratively aligns a larger portion of the audio partitions and thus increases the amount of data available for acoustic model (AM) training.


In each iteration all the partitions produced by the VAD undergo restricted decoding using the BioKIT toolkit: The search vocabulary is restricted to the vocabulary of the audio recording transcription plus fillers.

we can make use of the transcriptions which originally could not successfully be aligned with the audio.


Regarding the latter, (Matveev, 2013) show a clear trend of degradation of the performance of automatic speaker recognition systems in a time interval of up to 4 years.

both inter-and intra-speaker variability should be studied in order to have a reliable assessment.


Both of them are based on corpus and the quality of the produced voice depends heavily on the variety and quality of the corpus.

it is essential to have a suitable database of singing voice that is sufficiently representative to obtain good synthesis results.


Standard diarization techniques applied to singing are not completely suitable, as the system tends to identify different speakers when the singer changes note.

the result of the classic diarization system has been postprocessed to group all the different identifiers corresponding to the same singer.


In exercises with point, where the host starts singing the bertso and the bertsolari has to continue, the results of the automatic speaker segmentation are incorrect as the diarization system is not prepared to detect the same person singing and speaking.

this type of exercise would require a more elaborate diarization algorithm.


The measurement of the Kinects' positions with a laser distance meter by hand introduced a small systematic error.

we decided to make use of some prior knowledge: all speakers were walking on marked trajectories.


Moreover, the visual evaluations of the videos revealed that all speakers were walking on the trajectories without noticeable deviations.

we computed the squared error between each detection and a fine grid of points on the trajectories.


We knew that there will be clockdrifts and synchronization problems between the boards, because they were not connected to a central word clock (this was a hardware-restriction).

we set up the 24element circular array in a way that eight MEMS microphones connected to one board represent an 8-element circular array with constant angular interval of 45 • ; considering all three circular arrays, the merged 24-element circular array exhibits an interval of 15 • .


It was difficult for the speakers to think of a topic during the recording sessions that would contain the spoken commands that we had envisioned.

we instructed speakers prior to the recording about possible topic areas (e.g., commenting a news article, answering to a tweet, composing an informal SMS message, composing an e-mail message that they would have to send during the day, etc.)


Dysarthria is a speech disorder caused by difficulties in neuromuscular control (Duffy, 1995) that lead to decreased speech intelligibility and communication impairment (Kent and Kim, 2003).

the life quality of dysarthric patients is negatively affected (Walshe and Miller, 2011) and they run the risk of losing contact with friends and relatives and eventually becoming isolated from the society.


In Urdu, space does not always mark word boundary.

we created two versions of the same corpus.


Automatic summarization can be further improved by preprocessing work, such as normalization of text, part-ofspeech tagging, morphological analysis, lemmatization and stemming (Leite et al., 2007;Torres-Moreno, 2012;Torres-Moreno, 2014;Nuzumlal andÖzgür, 2014;Eryigit et al., 2008).

we further apply the needed software tools and provide the annotated output corpus freely.


If a word ends with a non-joiner the shape of morphemes remain intact whether space is inserted between them or not.


Lastly, Urdu speakers tend to disagree whether compound words are one word or more (Durrani and Hussain, 2010).

we do not mark compound words as single words.


These lexical functions are then connected with the lexicon which is built semi-automatically, and further, manually checked for mistakes.

there seem to be less possibility of a word to be incorrectly analyzed (and lemmatized) with this tool if it is present in the lexicon.


Similar data is also shown by other applications, such as Viber 3 , WeChat 4 , LINE 5 , and KakaoTalk 6 .

summarization system becomes important to ease people in extracting the information automatically and quickly from chat conversation.


Vary and active conversations are more often found in multi-participant than one-on-one conversation.

we utilized 10 logs of chat conversation group as our raw dataset.


Even though ROUGE is a very famous approach for summarization evaluation, however it is only an n-gram matching of two documents.

comparing the summary and abstractive reference summary can give a low score.


it is important to study the validity of existing methodologies applied to the evaluation of news article summarization for this task.


For scientific summarization, we consider the context of the words as the scientific articles in which they appear.

if two concepts appear in identical set of articles, they are semantically related.


Since TAC 2014 benchmark is on summarization of biomedical articles, the appropriate index would be the one constructed from articles in the same domain.

we use the open access subset of Pubmed 2 which consists of published articles in biomedical literature.


In the TAC 2014 summarization track, ROUGE was suggested as the evaluation metric for summarization and no human assessment was provided for the topics.

to study the effectiveness of the evaluation metrics, we use the semi-manual Pyramid evaluation framework (Nenkova and Passonneau, 2004;.


The intuition is that, if a nugget occurs more frequently in the human summaries, it is a more important contributor (thus belongs to higher tier in the pyramid).

if a candidate summary contains this nugget, it should be rewarded more.


Very few teams participated in TAC 2014 summarization track and the official results and the review paper of TAC 2014 systems were never published.

to evaluate the effectiveness of ROUGE, we applied 9 well-known summarization approaches on the TAC 2014 scientific summarization dataset.


Our aim is to analyze the effectiveness of the evaluation metrics, not the summarization approaches.

we consider the correlations of the automatic evaluation metrics with the manual Pyramid scores to evaluate their effectiveness; the metrics that show higher correlations with manual judgments are more effective.


Adopting a more pragmatic view on argument structure also has the advantage that it is suitable for annota- tion or validation of automatic output using crowdsourcing 13 , which is a commonly used method for evaluating Human Language Technology (HLT) systems (Callison-Burch, 2009;Passonneau and Carpenter, 2013).

the crowdsourcing Human Intelligence Task (HIT) was designed as a validation task (as opposed to annotation), where each system-proposed link and labels are presented to a human contributor for their validation with both article sentence and comment sentence placed within context (see Fig.


We have ensured a fully random selection of verbs to annotate.

we can generalize our PDEV-on-BNC annotation results beyond our sample, at least regarding PDEV entries with no less than three patterns.


Spelling became a political issue in Germany through the German orthography reform, which was decreed in 1996 and caused a fierce controversy carried out in public as well as uncertainty even among professional writers about the correct spelling.

of this tumult caused by the reform, the Council for German Orthography (Rat für deutsche Rechtschreibung) was established in 2004.


For example, context words are an important group of features in our classifier and they are sensitive to topics that might be more prevalent in some time periods.

we checked whether data from a certain range of years are labeled appropriately by  a classifier that is trained on data from previous years, or whether a classifier has to be retrained on training data from the same range of years.


But in practice, the opposite seems to be the case, due to actual mentions being more heavily skewed towards popular entities than in WSD, a plethora of available training data in the form of human-entered anchor-texts of hyperlinks on the web, and higher inter-annotator agreement, which indicates more crisp differences between possible shades of meanings than in WSD.

both popularity-based dictionary lookups (MFS heuristics) and supervised classifiers, which are traditional WSD architectures, perform better for NED than for WSD.


These links are very useful, they help us to improve our Portuguese WordNet.

we discuss the integration of these links in our base and the issues we encountered with the integration.


Rather than simply attempting to manually project the verb-noun pairs between words forms from Nomlex-PT to links between particular senses of those words, a difficult task given the fine-grained nature of Princeton's synsets and the state (not fully curated) of our Portuguese OpenWordnet-PT, we decided to use Princeton's morphosemantic links as helping data to both discover issues with OpenWordNet-PT synsets and to help to project the links from NomLex-PT.

the work we describe here consists in adding to the pairs of translated morphosemantic links, that is to pairs of senses of verbs/nouns in Portuguese, a label from Princeton's table and making such a triple, a link of the OpenWordnet-PT.


However, there are no models that describe the domain of morphology in a similar manner.

there are hardly any language resources of morphemic data available in RDF to date.


This overview of morphological resources reveals a gap between the existing non-Linked Data resources and the available Linked Data models.

the current landscape of morphology consists of isolated but extensive non-RDF resources on the one side, and interoperable Linked Data vocabularies which are insufficiently expressive to model morphology, on the other side.


The data has been analyzed, integrated and transformed to the MMoOn Core and the specific Hebrew schema using a custom data integration pipeline.

the data has been cleaned according to formal criteria.


The knowledge contained in these works is very valuable, but non-existent in a digital format.

retrieving information about words that share the same root, word-forms that belong to one lexeme or which morphological patterns are used in word-formation is bound to tedious and timeconsuming manual search through books.


Both lexemes in Figure 3 consist of roots from which the word-forms of the Barkali Pi'el group 1 have been built.

all of these word-forms are related to the same set of transfixes, since they are in the same Binyan group.


We showed that MMoOn is suitable for describing complex morphemic elements and their relations even for languages, such as Hebrew, which deviate from traditional Indo-European word-form analysis.

the Hebrew Morpheme Inventory represents a novelty among the current language resource landscape by expressing fine-grained morphemic language data in conformity with Linked Data modelling standards.


At this stage, we took advantage of the fairly homogeneous HTML structure of the crawled web documents to identify the actual content, remove boilerplate, and export text segmented into paragraphs.

we obtained a total of 2.50M paragraphs, of which approximately 293K were annotated in the exported files as in a language other than the main language of the document.


In addition, during the experiment described here, we ommited a tool's method that exploits special patterns in URLs and links that point to candidate translations.

we only used methods that are based on a) cooccurrences of images with the same filename in HTML source, b) edit distance of sequences of digits in the main content of webpages and c) structural similarity.


"quick reads" and longer articles), we examined how these metrics are affected by the length of document pairs (in terms of total words in the main content of both documents in a pair).

we counted the number of real, detected, and correctly detected pairs (GT, Detected, and Correct columns in Table 5 respectively) with a length higher than the number in column Pair Len.


Very little work has been done on polarity lexicons for Basque, as is the case for other less resourced languages.

when facing the task of creating such a resource, the doubt arised.


For example, the verb kataba "to write is obtained by attributing the verbal scheme fa'ala to the root ktb, the noun maktab "desk is formed attributing the scheme maf'al in which the prefix ma indicates the event location.

each unit is defined by two criteria: a) a morpho-semantic criterion, namely, the root and ii) a morpho-syntactic criterion namely the scheme (Kouloughli, 1994).


We chose to represent AQAM in LMF so as to make the information contained within the lexicon available in a structured form both for lexicographic research and for NLP applications.

not only will it be possible for researchers 3 The schema is a syllabic pattern in which the root consonants (R) occupy a specified place; derivational affixes may be inserted at specified positions; but also the length and tone of vowels are specified.


Arabic roots, in fact, group lexemes in semantic classes, but are not themselves provided with a specific meaning.

of this by adding roots as lexical entries lexicographers are promoting what is just a device to group semantically related words in printed dictionaries into actual words, thus inflating the amount of "real" entries in the actual lexicon.


Most probably this activity will only increase in the current Digital Humanities era.

of the past and current work we have lots of digital historical document collections available and will have more of them in the future.


In our case, the size of the collection makes comprehensive human inspection impossible: almost 2 million newspaper pages of varying quality cannot be assessed by human labour.

quality assessment of OCRed collections is most of the times sample-based, as in the case of the British Library (Tanner et.


for the rest circa 765 M of tokens the recognition rate is very low, only 30 M of them are recognized.


The biggest and most visible difference between modern Finnish and 19 th century Finnish is variation of w/v, which does not exist in modern language.

words that have w and are not proper names like Wien (Vienna in Finnish) are not usually recognized by modern morphological analyzers.


To resolve these, TICCL will have to be equipped to be able to handle and correct word bigrams as we equipped our prior system TISC (Reynaert, 2005) to do.


The role of an adjuvant is to dilute X1, and to allow to inject it to patients P. From the formal role of 'vaccine', the Qualia of the concept 'medicine' is included into the network: Medecine is not exactly a super-type for 'vaccine', it is more shallow a notion.

a property inheritance is not systematic as it is in an 'isa' relations.


(2) since Y and X1 are parts of X, and Y is injected with X1 (from the telic of adjuvant), then since Y is toxic for humans, it follows that X is also toxic for humans.

argument A1 has a negative orientation, due to the negative polarity of toxic (lexical feature of the adjective), therefore A1 attacks the controversial issue.


Quantitative contrastive analyses on the level of discourse require annotated corpora involving timeconsuming compilation and annotation, especially in a multilingual setting.

we take advantage of the existing resources reflecting systemic peculiarities and realisational options of the languages under analysis.


Annotation of textual coreference contains not only relations of identity between entities but also abstract and situation anaphora.

we may coreference to nominal phrases (NPs) along with coreference to clauses, clause complexes and larger textual chunks, as illustrated in example (1-a) for German and (1-b) for Czech.


Where a witness report has been captured and kept as an audio recording, there is important additional information that can be learned from the way that things were said, that are not conveyed through the wording, but by making use of various paralinguistic means (prosody, pitch, volume, intonation).

in spontaneous speech, silences and filled pauses (like 'eh') are important cues to a speaker's emotional state and involvement (Tisljar-Szabó & Pléh, 2014).


Through the above analysis, it is obvious to realize that developing multilingual discourse corpus is quite necessary for multilingual NLP tasks and helpful to understand the diversity of discourse information in multilingual environment.

at the start point, we propose and annotate a bilingual discourse corpus.


This is to say a well-formed dependency substructure headed by a verb is usually semantic integrity.

we define two types of BEDU as follows: The first type is hard-BEDU, a source-side span and a target-side span compose a hard-BEDU if and only if the source and target span are translations with each other, are both well-formed dependency substructures (dominated by a verb1) and the source or the target span should be an EDU in the monolingual language.


In machine translation community, there are large-scale parallel sentence pairs.

we first annotate bilingual discourse structures on this data.


Within each syntactic category, as explained above, PDTB senses are taken to motivate the semantic reading(s).

for every syntactic category we decide whether more than one PDTB sense can be assigned; each of these generates a <sem> field within the corresponding <syn> field.


Since our ultimate goal is not a corpus of annotated instances, but an enriched lexicon, we only need to obtain a set of senses that each connective can express.

a high Jaccard index 7 indicates that the sets of senses chosen by 7 The Jaccard index between sets A and B is defined as |A ∩ B|/|A ∪ B|.


For example, one annotator identified several "contrastive" or "concessive" uses of gleichzeitig ('at the same time'), where the other annotator saw the basic temporal synchronicity sense as sufficient.

we discussed all connectives with the entire team of three annotators and decided difficult cases by consensus.


Looking at the categories that CCR distinguishes but the PDTB doesn't, it is revealed that the PDTB often does not distinguish between the source of coherence in its relation labels.

most relation labels are still underspecified regarding their source of coherence.


The same goes for CCR's order: PDTB has encoded Arg1-Arg2 order in their labels, but does not take into account the surface order of their arguments.

the order of the segments remains underspecified for most PDTB labels.


Similarly, one of the two days corresponds to a day strike in the Parisian metro.

the most common theme concerns the traffic and conversations are mostly noisy since many users call a station not seeing their bus or Metro come.


segments) are designated with numbers (1 through 5) while Complex Discourse Units are represented by π 1 and π 2 , dashed lines indicating member-hood in a CDU.

we consider an SDRT discourse structure as a graph, , with a set V of discourse units; two types of edges, E 1 (relations) and E 2 (CDU membership), with E 1 , E 2 ⊆ V 2 ; and a labelling function : E 1 → T , where T is a set of discourse relation types.


As an artificial language with a focus on regularity and facilitation of language acquisition, Esperanto was designed with a morphology that allows (almost) free, productive combination of roots, affixes and inflexion endings.

the root 'san' (healthy) not only accepts its prototypical adjectival ending '-a', but also other part-ofspeech endings ('san|e' -healthily, 'san|on' -cheers) 1 , as well as prefixes ('mal+san|a' -unhealthy [mal=opposite]), suffixes ('sanigi' -cure [ig=make]) or multiple affixes ('mal+san+ul+ej|o' -hospital [ul=person, ej=place]).


However, what is transparent for a human beholder, is not necessarily as easy to grasp for a computer program.

while POS is completely unambiguous in Esperanto, unrestricted compounding and affixation do produce a fair amount of theoretical morphological ambiguity.


Like Thai, Japanese and Chinese text, Vietnamese text is also a text without any explicit separator between words.

identifying the word boundaries is a challenging basic issue to above oriental languages for natural language processing (Doan, 2008).


In raw Vietnamese texts, space symbol can be treated as an overload symbol, which is a connector within a word or is a separator between words.

the Vietnamese word segmentation (VWS) problem can be defined as a binary categorization task for each space symbol.


Under the supervised condition, the labeled training corpus, with the same distribution to the unlabeled testing corpus, can help to predict dictionary performance.

we propose a square overlap ratio (SOR) measure to predict the performance of dictionary.


One of the most convincing examples here is Icelandic with its many ways to productively build NNCs, but clearly also German and English.

the task is not only to identify non-transparent NNCs (which are actually less interesting from a linguistic point of view) but transparent ones built on the fly by productive rules.


A simple substitution to umlauts is not possible, as other roots such as aero 'aero' do not require this change.

in order to avoid time-consuming post-editing, the components of morphological analyses have to be adapted by heuristics and manually checked.


The lemmas for which the second rule applies are difficult to identify in a list of over 50,000 entries.

the substitution rules for double to triple consonant clusters are derived semi-automatically and incrementally from the morphological part of the lexicon.


• Some of the derivations have a strong emphasis on diachronic derivation as shown for Verschlüsselung 'encryption' in Figure (5) where the noun Schlüssel 'key' is analysed as a derivation from the verb schließen 'close' which is certainly justified but not adequate for every task.

the frequencies should be augmented or replaced by ones from reliably lemmatized corpora.


Evaluative adjectives express a "sentiment" of the speaker/writer with respect to the entity to which the adjective is applied.

"a great car" expresses a positive sentiment, while "a lousy car" expresses a negative sentiment.


Since modifiers are important to our investigation, our approach favors longer patterns that can be applied.

for the phrase "il a une très bonne odeur" (it has a very good smell), "très bonne odeur" (very good smell) is a target phrase rather than "bonne odeur" (good smell).


Train  Test  Train  POS NEG NEU Total POS NEG NEU Total + Test   RESTAURANT   GENERAL   175  214  14  403  58  63  4  125  528   PRICES   30  60  12  102  9  10  4  23  125   MISCELLANEOUS   27  62  17  106  9  19  6  34  140   FOOD   QUALITY   332  340  43  715  137  128  22  287  In an effort to keep the training and test datasets as balanced as possible with respect to the polarity of the opinions expressed in the texts, we collected an equivalent number of user reviews with different rankings based on a 5-star rating system (excellent, very good, average, poor, terrible).

the retained reviews are uniformly distributed over all possible ranks.


There is no existing resource for Russian and Farsi affective norms.

we ran only the validation using workers recruited through Amazon Mechanical Turk.


(2007) for affect values for Spanish words, and there are no resources for Russian and Farsi words.

the results of the present study should be of high interest to the scientific community.


With the basic emotions (happiness, sadness, fear, anger, disgust and surprise) accepted as the state-of-the-art, existing emotion corpora and other language resources that serve as the basis for building and evaluating mechanisms to detect emotion in tweets are only annotated with those basic categories.

automatic emotion detectors developed using these resources are only able to give us a limited picture of human emotion expressions.


In particular, we learn from ConceptNet (Singh et al., 2002) properties of objects, actions and phenomena which cause certain emotions.

these properties can be used to detect expression of emotions.


Since these words are semantically different apart from their relation to the "fear" emotion, their common properties are most probably related to this emotion.

"dangerous" may be considered semantically related to the emotion "fear".


In contrast to SentiWordNet and other sense-based sentiment resources, we found that a lot of Russian ambiguous words have the same polarity in all available senses.

it is simpler to assign the sentiment polarity information to such a word and not to enumerate all senses of a word with the same sentiment information.


Negative phenomena are struggled with, fought against, etc.

we supposed that words with connotations are mentioned in specific contexts.


Other words that were met in patterns of both classes were considered as neutral.

the extracted words and phrases were subdivided into three classes: words with negative connotations (most patterns were negative), words with positive connotations (most patterns were positive), and neutral words without specific connotations (could be equally met in both types of patterns).


Distribution of messages according to polarity classes in the SentiRuEval-2016 datasets After the label propagation, the whole volume of the RuThes thesaurus entries obtained positive, negative, or neutral labels.

the Extended Connotation Lexicon was generated.


The test collections were gathered in two parts: during July 2015 and November 2015.

there is a considerable time gap between training and text collections of SentiRuEval.


The original paper on PVs from Le and Mikolov (2014) reported an error rate of 7.42%, but subsequent experiments (Mesnil et al., 2015) declared that "92.6% accuracy result only when the training and test data are not shuffled.

we consider this result to be invalid.".


Of course, when the goal is to extend an opinion lexicon, several seed words of a same class can be used (not just one as in the previous experiment) to find new words sharing hopefully the same sentiment properties.

it is also interesting to examine how the performance evolves according to the number of words used as queries.


While such a visual representation certainly offers a good overview, the dimensional reduction comes at the cost of information loss, leading to distortions in the displayed distances of vectors in the low dimensional space.

there is a risk of misinterpreting semantic relatedness of feature vectors, especially if specific word vectors are compared (Chuang et al., 2012).


Except for the baseline, all of the other models use a 5 Since our model predicts compositionality, and the human judges annotated non-compositionality, we inverted the model predictions.

measures based on precision focus on selecting non-compositional compounds.


Detecting non-compositionality, on the other hand, can be somewhat more complicated: while non-compositional expressions such as think tank are being correctly classified, other MWEs such as carnival crowd and background target are simply not frequent enough in the corpus.

as the distributional vectors were built based on limited occurrence contexts, the compositionality predictions are not reliable.


With this strategy, a monolingual similarity method is applied after having (one of) the target sentences translated into a pivot language.

this paper specifically details the required and developed resources to implement this framework, while presenting our current results for English-Japanese-Chinese cross-lingual STS tasks that may exemplify the validity of the framework.


• N -grams could provide more informative evidence of sentence similarity.

as in (Saric et al., 2012), we calculate three types of N -gram overlap-based similarities Sim f 3∼f 5 (for N = 1, 2, 3, respectively), which are computed by the following formula.


• It is confirmed that the quality of machine translation matters.

machine translation engines with better translation qualities should be utilized; the pivot language should be one of the language of the target sentences (avoid more than one translation processes) where possible.


Users may further find more alternatives to complete the task by directly employing patterns such as "how to" + task description.

potential how-knowledge is implicitly embedded in search query logs.


How to Lose Weight is a title of a document which contains 5 step titles, including Keep your own personal food diary and determine your weight loss goals upfront, Have a balanced diet, Avoid skipping meals, Eat food from home, and Learn to love fruit.


Automatic assignments are useful for decision making only if they are very precise.

we investigate ways of combining the predictions of the five classification models, to increase precision.


Due to the sparsity of positive training instances for the low-performance research topics (table 3) the classification models rarely produce positive predictions.

requiring a positive prediction from three, four or five models is a very strict criterion, affecting performance negatively.


Better understanding of the participants and what they do, how they behave can make these online communities more successful.

the problem is, given two revisions of a piece of text, what actions have been done by a user to transform the original version into the new one, and who that user is?


For example, a user who always works on copy editing and rephrasing might be a copy-editor; one seems to be a substantive expert if he/she contributes to information insertion a lot.

our edit taxonomy enables us to identity user roles based on their elemental actions.


The expanding use of social media and electronic forms of written communication has been accompanied in the Arab world by an increase in the production of written dialect.

there is a growing interest in NLP tools for written Arabic dialects, for example from companies that would like to perform sentiment data mining on regional web forums to determine what consumers think about their products.


It has been shown that similar words tend to be closer to each other in this high-dimensional space and that these similarities can of both semantic and (morpho)syntactic type (Mikolov et al., 2013b;Mikolov et al., 2013a).

we built two types of models using the word2vec 3 tool, the widely-used framework for creating word embedding representations.


Throwing these words away, the algorithm was applied again resulting in purer lists.

starting from one word for each category, hundreds or thousands of related words could be retrieved semi-automatically with minimal human interaction that could hardly have been done manually.


In French, fricatives and plosives at the end of words can also occur as voiced consonants -in contrast to German where final devoicing applies.

interferences on the segmental level can be expected when French native speakers learn to speak German and vice versa.


This results in a book-length list of segmentation rules that often involve semantics (Ogura et al., 2011).

it is virtually impossible to automatically adjust segmentation of external resources.


Although disambiguation is useful for appli- cations, it is beyond the scope of word segmentation.

when Wikipedia is converted into a word dictionary, we have to waste effort merging entities with the same name.


We also excluded disambiguation pages, which simply listed possible referents.

we obtained 2.5 million unique mentions for 909 thousand entities.


Users browsing a dictionary are often interested in particular valence phenomena rather than concrete entry representation.

slowal allows to use a filtering form (cf.


Given the good results obtained with the adaptation of the automatic tagger to child and child-directed speech, the same process was applied to the Freitas database.

of the POS-tagging, each transcription file of both corpora has a morphosyntactic tier, which is the output generated by the tagger.


We should insist on the fact that, apart from blending certain categories along the lines described in this section, no other changes were made to the POS-tag classes generated by the automatic tagger.

what is presented in table 2 corresponds to the subset of the POS-tag list originally used by the automatic tagger which was maintained in CEPLEXicon.


All of these tasks are open research questions, which makes large vocabulary SLR a challenging problem.

these corpora often contain few users, but a higher number of repetitions per user to improve recognition performance (Cooper et al., 2011).


The fall choreography is influenced by what the individual wants to show to, or hide from, the device.

the risk of non-detecting these movements is possible, because they sometimes tend to veer too far from programmed scripts.


This can therefore lead to a denial of risk and recognition of the elderly individual's status of "victim", thereby throwing suspicion on their statements.

cIRDO may operate a transfer of the risk recognition: It is the technology that gives official status to a fall in a domestic incident.


(Povey et al., 2011a) showed that the model is also effective with large amount of training data.

three UBMs were trained respectively on SWEET-HOME data, Voix-détresse and clean data.


There are a number of limiting factors, among them standardization and difficulties in manual annotation.

the creation of modern language corpora builds on the development of proper tools for automatic sign language annotation.


An average of about 9 hours is spent for annotating 1 minute of recorded signed discourse -just primary annotation, nonmanual components and secondary annotation not included.

at the moment we are we are exploring methods of automatic or semiautomatic recognition that can assist in the annotation of the outstanding recordings or in future stages.


was designed to support the evaluation of computational models addressing several language acquisition tasks, in particular the acquisition of grounded syntactic patterns.

they predefined objects and actions, reappearing several times.


Additionally, speakers who do not get feedback from addressees take longer and make more elaborate references (Krauss and Weinheimer, 1966).

we employed head-movements of the robot (so that the speaker was able to infer its eye gaze) and verbal backchannel feedback.


The chosen coding scheme for gestures was adapted to the requirements of the corpus which comprises mainly object manipulation and deictic gestures.

in the coding scheme deictic, iconic, beat, emblem and poising gestures produced by the teacher are manually annotated.


The action must be related to the board, such as " " (sacrifice).

this does not include other player actions like "close eyes."


Gestures for Human Computer Interaction (HCI) are usually confined to navigation commands or task-oriented hand and arm movements.

hCI gestures either come as manipulators or as part of a specific gesture lexicon (semaphores) (Quek et al., 2002) (cf.


A system that suggests automatic behaviors for the user is therefore suggested as a possible solution to accomplish satisfying home automation routines.

a smart environment has to be able to offer users a set of functionalities based on their behavior.


because of a broken light or because it is dark outside) it could apply the other rule.

by adding logical or semantic information to our database learning could be enhanced.


Single action verb in sentences having the same argument structure can denote to several types of actions.

the concerned action verb does not explicitly specify the entity that it signifies as put forth by (Ishibashi, 2012 andVerma, 1999).


Naturally, PTB annotations also differ in linguistic analysis and their OLiA representations have less overlap with MLG gold triples.

the recall drop is not surprising for the English normalisation.


Deviations from this pattern are taken as an indication of spurious triples, as are triples with scores close to 0.

we tested whether the system could benefit from admitting only paths whose triples' activation exceedes a minimal path selection threshold.


The corpus collected for this study is set in an ecological microworld, as we need to observe gestures spontaneously produced by humans in a real HRI situation (Guillaume & al, 2015).

to train an HRI system to natural gestures should not impose a gesture language to the human.


It has been partially confirmed by the free comments of the subjects which express some positive attachment when they watch their subtle gestures (for example: "I kindly show it/him").

we propose the subtle gestures (with lax movements) as characterizing the glue effect as well as the increasing confidence in robot understanding.


In the case of the present paper, however, the scenario is different because we integrate the resulting hyponym-hypernym pairings with the CPA Ontology.

of the application of this first step, the CPA ontology, which initially contained a set of 250 semantic types, has grown to a total of 2,290 categories, each one representing a parent node populated with hyponyms.


Instead, it builds a table of correspondences between semantic categories and morphological features, which are simply defined as sequences of 3-5 letters at the end of each word.

it takes the output of the previous algorithms as input and then associates, for instance, that terms ending with charecter sequences such as -itis or -osis are consistently classified as diseases.


and extracted random samples of 500 to 1000 words from the corresponding transcriptions.

the GOLD standard is relatively balanced concerning the interaction types sampled in the corpus: 41.6% of the transcripts contain clearly non-standard, mostly regional language, 46.7% contain standard language, and in 11.8% of the data some speakers talk in standard High German whilst their conversation partners speak in a regional variant.


in annotating the function of word forms rather than just their morphological form, and as coarse-grained as necessary in order to minimize pragmatic interpretation and resulting inconsistencies in manual annotation.

some word classes had to be grouped together under one category, especially in cases where the annotation would have strongly relied on the interpretation of the annotator or where word forms actually are ambiguous, for example backchannel signals, responsives and interjections (NGIRR).


There are millions of tweets daily, yielding a corpus which is noisy and informal, but which is sometimes informative.

twitter has become one of the most important social information mutual platforms.


However, the performance of standard POS taggers for English is severely degraded on Tweets due to their noisiness and sparseness (Ritter et al., 2011).

pOS taggers for English tweets have been developed such as ARK, T-pos and GATE TwitIE which reaches 92.8%, 88.4% and 89.37% accuracy respectively (Derczynski et al., 2013).


They fail to deal with Twitter phenomena.

their outcomes are not useful to be used in linguistics downstream processing applications such as information extraction and machine translation in microblogging domain.


As a result, their outcomes are not useful to be used in linguistics downstream processing applications such as information extraction and machine translation in microblogging domain.

there is a need for a POS tagger which should take into consideration the characteristics of Arabic tweets and yield acceptable results.


difficult to detect all slangs in tweets domain.

we select the most frequent twenty slang words from 17k types in our corpus (10 million tokens) and map them to their IV equivalents as shown in Table 5.


For example, RTS tagset uses one tag (RP) to cover a range of particles which are subdivided to nine subclasses by MADA (part det, part focus, part fut, part interrog, part neg, part restrict, part verb, part voc ,part); and it uses several tags to distinguish between verbs tenses (VBD, VBG, VBN, VBP) where MADA just uses one tag (verb).

mapping between these tagsets is a prerequisite for using agreement-based method on the taggers' outputs.


• Finally, run times of systems may be of considerable interest for practitioners.

we include both training and testing time estimations of the different techniques.


, 3: suffixes often inform about grammatical features of the corresponding word and, thus, about its PoS.

we extract suffixes as features using a CRF-based stemming method.


As mentioned above, our purpose for word segmentation is to build a treebank for Vietnamese.

we consider a word as the smallest syntactic unit having a complete meaning and preventing syntactic rules from analyzing word structure (Sciullo and Williams, 1987).


The number of the positions where an error is corrected is 466.


On the other hand, Estonian punctuation rules are rather strict and interpunctuation marks are used extensively.

we decided to focus on syntactic annotation of words and deal with the analysis of punctuation marks later.


The concept seems to be well adapted for citing Language Resources although the current model does not take into account Language Resources, nor ISLRN number.

the idea is to make use of a BibTeX entry that could be used for bibliographical references and that would take into account Language Resources items.


Dealing properly with the presence of ironic devices is indeed crucial when the goal is to analyze the polarity of the opinions on a topic of interest, and in political domains irony is very often used in conjunction with a seemingly positive statement, to reflect a negative one, due to a phenomenon known in literature as polarity reversal (Bosco et al., 2013).

the issue has to be tackled in order to correctly label the polarity of an opinion, going beyond the literal meaning of the posts.


Several personality typologies exist, however, the Myers-Briggs Type Indicator (MBTI) is particularly popular in the non-scientific community, and many people use it to analyse their own personality and talk about the results online.

large amounts of self-assessed data on MBTI are readily available on social-media platforms such as Twitter.


All of these may be expressed by a variety of different linguistic structures.

an analysis of CS data requires inputs at Social, Contextual, and different linguistic and meta-linguistic levels that operate on various sub-parts of the conversation.


The definition of a word unit is indispensable in UD annotation, which is not a trivial question for Japanese, since a sentence is not segmented into words or morphemes by white space in its orthography.

we have several word unit standards that can be found in corpus annotation schemata or in the outputs of morphological analyzers (Kudo et al., 2004;Neubig et al., 2011).


And we use compound to label other types of multiword lexemes, with headedness according to the language and/or compound type.

compound is used for any kind of X 0 compounding: noun compounds (e.g., phone book), but also verb and adjective compounds that are more common in other languages (such as Persian or Japanese light verb constructions); for numbers (e.g., three thousand books gives compound(thousand, three)); for particles of phrasal verbs (e.g., put up: compound(put, up)).


Nevertheless, this method often leads to the following problem: A node derived from an MWE could have multiple heads and the whole dependency structure including MWE might be cyclic.

we converted a phrase structure to a dependency structure after establishing an MWE as a single subtree.


In dependency structure that takes MWEs into consideration (i.e., MWE-aware dependency), each MWE becomes a single node.

to convert word-based dependency to MWE-aware dependency directly, one could combine nodes in an MWE into a single node.


In Figure 1 for instance, "a number of" has multiple heads ("cities" and "have"), and a cycle occurs because of the following edges: "a number of" "cities" and "cities" "a number of" ( Figure 1b ).

we need to remove one of the edges in order to get a dependency tree.


Compared to modern Chinese, fewer words in medieval Chinese text contain more than two syllables.


Moreover, most work on WSD has focused on English and has relied on WSD datasets built as part of the SemEval (Senseval) evaluation exercises (Navigli et al., 2007;Agirre et al., 2009;Manandhar et al., 2010;Moro and Navigli, 2015).

wSD datasets for languages other than English are comparably rare.


Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement.

we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses.


In our first step using Babelfy, we provide disambiguated instances for all content words (including multi-word expressions) from BabelNet and also for overlapping mentions.

the output of our first step, even if not perfectly accurate, may be considered to have full coverage in comparison with our refinement step.


Since we want to test the performance of our system specifically for low-resource languages, no suitable evaluation data could be obtained from any standard tasks.

we constructed a small evaluation corpus containing 20 ambiguous lemmas selected from a set of high-and mediumfrequency words in Norwegian Nynorsk, in the style of the English lexical substitution task (McCarthy and Navigli, 2009).


Due to the rapid growth in publications, researchers are faced with the problem of having to extract relevant information out of an enormous number of journals, conference papers and workshop proceedings.

the demand for a deeper, machine-driven understanding has gained interest in the research community, with applications such as summarisation, information extraction or citation analysis.


The dependency annotations scheme in Hindi treats a sentence as a series of modifier-modified relations.

the root of the dependency tree would be the primary modified of the sentence which is generally the main verb of the sentence.


Using both sets of features further improves performance over baseline(73.05% to 90.05%).

using purely lexical features we have achieved an f-measure of 90%.


Discourse connectives behave differently according to the grammatical category they belong to.

we explore modelling each set independently we trained specific models for each of the six categories of discourse connectives present in HDRB namely coordinating conjunctions, subordinating conjunctions, subordinators, sentential relatives, adverbials and particles.


Most commonly, this distinction assumes that Topic is the referent that the sentence is about, whereas Focus is what increases our knowledge about the Topic (Lambrecht, 1996).

topic can be characterized as given, whereas Focus as new discourse information.


The mainstay of Chai and Jin (2004) proposal is that the informational perspective of discourse should capture the semantics of the conveyed information.

topic and Focus are linked with the semantic roles of the constituents in the question in terms of its predicate-argument structure (Gildea and Jurafsky, 2002).


However, as mentioned, the main goal of our analysis is to estimate the theme that the query issuer is interested in, based on the sole context of the ISQs.

what we attempt to identify are the fixed and variable components of the query context, which we find more informative about the issuer's goals than the information that can be retrieved via sole pronominalization.


Lectures contain regions where speakers introduce some concepts, emphasise others, interact with students, and engage in other interesting discourse phenomena.

it is critical to locate these regions in order for a system to interpret lecture discourse.


Thus, for every category in the annotation schema, there are a total of 2,440 annotation tasks for the Physics lectures and 2,110 annotation tasks for the Economics lectures.

different instructions sets have been designed for each of the 19 MD category in the schema.


Similarly to the trail study, the decision of whether or not an utterance includes the discourse function of a certain MD tag is made based on the overall agreement between annotators; this is because the primary objective of this study is to detect if an utterance contains an instance of metadiscursive acts.

agreement between annotators is considered to exist if the intersection (in terms of number of words) between their annotations is not void.


And for each utterance, we also asked whether wa have an appropriate function in the scheme.

three annotators (with a computational linguistics background) performed several exploratory annotation sessions on a sample of conversations from November 2014.


Inversely, some of DIT++'s dimensions may not be necessary for written data.

the TIME MANAGEMENT class is irrelevant in the context of online communication (except some very rare utterances in chat).


With the arrival of Web 2.0, technologies like social media have become accessible to a vast amount of people.

they have become valuable sources of information about the public's opinion.


This is probably because along with diatopic variation, systems learn properties of datasets that are often topic or genre specific.

part of what was learned from the additional training corpora was not helpful for predictions on the test set.


The 2015 results were measured on all groups, but the 2014 system was not trained to recognized either group G or group X data.

in addition to the full 2014 and 2015 test sets, we evaluated performance on the subset of the 2015 test set that contains the groups in the 2014 shared task, i.e.


This worked well for websites that were solely/mainly for children, and for longer stories, where the URLs only pointed to children's material; but some websites are forums and contain all sorts of data.

using URLs was not appropriate for our purposes since everything would be extracted.


They are certainly contemporary; but as well as depicting ordinary life as we know it (school, teachers, holidays, home, parents, siblings etc), they also conjure a fantasy world of magical powers and beneficent/malicious creatures; and they are not even just children's books, since mums and dads (and people on the train) read them too!

our genre categorisation scheme is likely to be refined in subsequent versions of the corpus, since the ACC is an ongoing project.


we have one corpus with excess data and another corpus with scarce data for machine learning.


The idea is the following: the more P (c j |t) is different from P (c j ), the more important the term t i is.

we can calculate the term weight as the following: where C is a set of all classes.


For IDF the highest weight means that the term occurs only in one document from the training sample, for NTW it means that the term occurs only in documents of one class.

for these two methods we used different constraints for the value of weights.


For the first problem with k-NN the novel FT with collectives of term weighting methods provides the better result, than all other cases excluding the collective of term weighting methods with all words.

the novel FT is appropriate for real-time systems.


We see a dearth of benchmark corpora being developed for South Asian languages (Baker and McEnery, 1999) especially Urdu.

to foster plagiarism detection research in the Urdu language, we need to develop standard corpus resources for it.


Due to the large number of discarded threads for a threshold of 3, we choose to adopt 2 as our threshold.

types that were assigned by at least two annotators are considered as gold standard, and are used to train machine learners, as described in the next section.


In the case of code-switching, the first factor is the average switching level per utterance, as calculated by inserting the C u given by Equation 6 into the average of Equation 5, while the second factor is the frequency of utterances that contain any code-switching (i.e., utterances with C u > 0).

arriving at Equation 10: where S is the number of utterances that contain codeswitching (0 ≤ S ≤ U ), and w s the relative weight attached to the switching frequency.


It is important to keep in mind that the code-switching corpus complexity measurement is intended to be independent of the languages contained in the corpus, while the per-formance of a language processing system of course also will depend on the actual languages, their relationship, and their annotation schemes.

the reported level of mixing in the ARB-ARZ corpus is quite low (only 15.94%, with C c = 17.06), but the "languages" involved are both Arabic dialects, so very closely-related, and hence the potential overlap between them is high, even if that has not been reflected in the annotation.


Searching publications and browsing language resource directories, one finds that several datasets comparable to the ACL RD-TEC already exist-for example the GENIA (Kim et al., 2003) or the CRAFT corpora (Bada et al., 2012).

it is likely that a curious mind points to these resources and asks 'why do we need ACL RD-TEC?'.


Note that from an ultimate perspective, any combination of words in a sentence can be a term.

if |W | is the number of words in a given sentence S, potentially there are 2 |W | terms in S. the claim that pe → 0 seems reasonable.


Using this measure, we check whether the annotations are indeed identical on both categories.

the reported F -score for item (a) sets the upper bound limit for the Overall performance.


Style Since machine translation output is fully automatically generated, it has its own style that may seem unfamiliar or unnatural although, in some cases it has an acceptable word order and conveys the meaning of the English source text accurately.

in such cases the style of writing should not be modified or improved as can be seen in Table 3.